Self-guided quantum tomography

TL;DR

Self-guided quantum tomography (SGQT) is an iterative, self-learning method that efficiently estimates quantum states by directly testing hypotheses, outperforming traditional data-intensive approaches in accuracy and robustness.

Contribution

The paper introduces SGQT, a novel self-guided, iterative quantum state estimation technique that improves efficiency and robustness over conventional methods.

Findings

SGQT converges rapidly to the true quantum state.

SGQT outperforms traditional tomography in simulation tests.

The method is robust to noise and experimental imperfections.

Abstract

We introduce a self-learning tomographic technique in which the experiment guides itself to an estimate of its own state. Self-guided quantum tomography (SGQT) uses measurements to directly test hypotheses in an iterative algorithm which converges to the true state. We demonstrate through simulation on many qubits that SGQT is a more efficient and robust alternative to the usual paradigm of taking a large amount of informationally complete data and solving the inverse problem of post-processed state estimation.